<p align="center"> <img src="pic.jpg" width="1000"> <br /> <br /> </p> # ASGN The official implementation of the ASGN model. Orginal paper: ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. KDD'2020 Accepted. # Project Structure + `base_model`: Containing SchNet and training code for QM9 and OPV datasets. + `rd_learn`: A baseline using random data selection. + `geo_learn`: Geometric method of active learning like k_center. + `qbc_learn`: Active learning by using query by committee. + `utils`: Dataset preparation and utils functions. + `baselines`: Active learning baselines from [google's implementation](https://github.com/google/active-learning). + `single_model_al`: contains several baseline models and our method ASGN (in file wsl_al.py) + `exp`: Experiments loggings. # Citing ASGN If you use ASGN in your research, please use the following BibTex. ``` @inproceedings{hao2020asgn, title={ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction}, author={Hao, Zhongkai and Lu, Chengqiang and Huang, Zhenya and Wang, Hao and Hu, Zheyuan and Liu, Qi and Chen, Enhong and Lee, Cheekong}, booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}, pages={731--752}, year={2020} } ```